Association between obesity and the prevalence of dyslipidemia in middle-aged and older people: an observational study

This study aimed to explore the link between various forms of obesity, including body mass index (BMI) and waist circumference (WC), and the risk of dyslipidemia among Chinese residents. We selected the study population through a multi-stage random sampling method from permanent residents aged 35 and older in Ganzhou. Obesity was categorized as non-obesity, general obesity, central obesity, or compound obesity according to established diagnostic criteria. We employed a logistic regression model to assess the relationship between different types of obesity and the risk of dyslipidemia. Additionally, we used the restricted cubic spline model to analyze the association between BMI, WC, and the risk of dyslipidemia. The study included 2030 residents aged 35 or older from Ganzhou, China. The prevalence of dyslipidemia was found to be 39.31%, with an age-standardized prevalence of 36.51%. The highest prevalence of dyslipidemia, 58.79%, was observed among those with compound obesity. After adjusting for confounding factors, we found that the risk of dyslipidemia in those with central and compound obesity was respectively 2.00 (95% CI 1.62–2.46) and 2.86 (95% CI 2.03–4.03) times higher than in the non-obese population. Moreover, the analysis using the restricted cubic spline model indicated a nearly linear association between BMI, WC, and the risk of dyslipidemia. The findings emphasize the significant prevalence of both dyslipidemia and obesity among adults aged 35 and above in Ganzhou, China. Notably, individuals with compound obesity are at a substantially increased risk of dyslipidemia. Therefore, it is crucial to prioritize the use of BMI and WC as screening and preventive measures for related health conditions.

for LDL-C, and 1.7 mmol/L for TG 9 .The China Million Population Project, conducted from 2014 to 2019, focused on early screening and comprehensive intervention for high-risk individuals with cardiovascular disease.This project reported a dyslipidemia prevalence of 33.8% among adults aged ≥ 35 years 10 , which is notably higher than the 18.6% rate reported in 2002 11 .An international study projected an increase of approximately 9.2 million cardiovascular events due to dyslipidemia in the Chinese population between 2010 and 2030 12 .
Previous studies have confirmed that obesity is a significant risk factor for dyslipidemia 7 .Research indicates that insulin resistance may be the underlying mechanism linking obesity to dyslipidemia.Insulin plays a role in breaking down TG-rich lipoproteins.When insulin resistance occurs, the clearance of these lipoproteins from the bloodstream is impaired, leading to increased TG levels.Elevated TG levels can trigger cholesteryl ester transfer protein activity, resulting in the production of TG-rich high-density lipoprotein particles that are susceptible to hydrolysis by plasma lipases, ultimately reducing HDL-C levels 13 .The relationship between obesity and dyslipidemia is not only influenced by overall obesity but is also closely linked to central obesity [13][14][15][16] .In particular, the prevalence of dyslipidemia in the general obese population is 3.0 times higher than that in the normal population, while the prevalence of elevated hypertriglyceridemia and low HDL-C in the central obesity population is 2.5 times and 1.8 times higher than that in healthy figures, respectively 17 .Current research, both domestically and internationally, has primarily focused on the relationship between types of obesity defined by a single indicator and dyslipidemia.However, there is a lack of comprehensive studies exploring the association between compound obesity and dyslipidemia.This study aimed to investigate the correlation between various types of obesity, including body mass index (BMI) and waist circumference (WC), and the risk of dyslipidemia in permanent residents over 35 years old in southern Jiangxi.Additionally, this study sought to assess the impact of different types of obesity on blood lipid levels and to provide intervention strategies focusing on a balanced diet, physical activity, and health education.This research serves as a foundation for the development of evidence-based prevention and control policies for obesity and dyslipidemia among residents in southern Jiangxi.

Study design
This study used a multistage sampling method to determine the required sample size.The formula used was n = × Deff , where p is the expected prevalence rate, d is the allowable error, α is the test level, and Deff is the design effect.For this study, α was set at 0.05, p was 33.8% based on a national investigation in China 10 , d was 0.1p, Deff was 2, and the expected response rate was set at ≥ 85%, then a sample size of 1734 individuals was deemed necessary.The data in this study were collected as the baseline of the Gannan Medical University cohort study conducted from 2022 to 2023.Multistage sampling method was used to collect participations in Ganzhou City.According to the varying levels of economic development, the 18 counties in Ganzhou are classified into high, medium, and low categories.From each category, 1-3 counties are chosen at random.Subsequently, 1-3 communities or streets are randomly selected from the chosen counties, with approximately 300 individuals sampled from each community or street.Questionnaires, physical examinations, and biochemical index tests were administered to all 2131 participants in the selected units.After applying inclusion and exclusion criteria, 2030 valid samples were included.The inclusion criteria were: (1) local residents aged 35

Information and biomarkers collection
This study primarily involved conducting questionnaire surveys, performing physical examinations, and collecting and analyzing biochemical indicators.Questionnaires were administered through face-to-face interviews using self-compiled questionnaires that covered general health conditions, smoking and drinking habits, physical activity, disease history, and other relevant factors.The physical examination focused on measurements of height, weight, and WC.Blood samples were collected after an overnight fast, between 7 a.m. and 10 a.m., and were transported to the physical examination center for laboratory analysis within 12 h.Biomarkers were analyzed using a Beckman Coulter AU5800 fully automatic biochemical analyzer.Techniques such as latex particle-enhanced turbidimetry, hexokinase method, GPO-PAP, CHOD-PAP, catalase scavenging, and surfactant scavenging were employed to detect glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), TG, TC, HDL-C, and LDL-C.The kits used for these detections were supplied by Medical System company.Questionnaires and physical examinations were administered by investigators who had received consistent training.All samples were processed by skilled medical personnel in the laboratory department.

Indicator definitions
BMI was calculated using the following formula: BMI = weight/(height) 2 .According to the obesity classification standards outlined by Zhang Siting et al 18 , non-obese individuals are characterized by a BMI < 28.0 kg/m 2 and a WC < 90.0 cm for men and < 85.0 cm for women.General obesity is defined as a BMI ≥ 28.0 kg/m 2 with a WC < 90.0 cm for men and < 85.0 cm for women.Central obesity is identified by a BMI < 28.0 kg/m 2 and a WC ≥ 90.0 cm for men and ≥ 85.0 cm for women.Compound obesity is defined as both a BMI ≥ 28.0 kg/m 2 and a WC ≥ 90.0 cm for men and ≥ 85.0 cm for women.
Dyslipidemia is defined as having TC ≥ 6.22 mmol/L, LDL-C ≥ 4.14 mmol/L, HDL-C ≤ 1.04 mmol/L, TG levels ≥ 2.26 mmol/L, or self-reported use of lipid-lowering medications, in accordance with the 2016 Chinese

Statistical analysis
Statistical Product and Service Solutions version 14.0 (SPSS 14.0) was used for data cleaning and analysis.Categorical variables were presented as constituent ratios or rates and analyzed using the χ 2 test.A logistic regression model was used to examine the correlations between various types of obesity and dyslipidemia.Given the potential for a non-linear relationship between BMI, WC, and dyslipidemia.The R software version 4.3.2 was utilized to develop a restricted cubic spline.The fundamental concept of a restricted cubic spline is to model the spline function, RCS(X), by strategically selecting the placement and number of nodes.This approach allows the continuous variable X to exhibit a smooth curve across the entire range of values.Following adjustment for confounding factors, three nodes of BMI and waist circumference (P 10 , P 50 , P 90 ) were selected based on the Bayesian information criterion to investigate BMI and waist circumference among the different sexes.The dose-response relationship with the risk of dyslipidemia was explored.A two-sided test with a significance level of α = 0.05 was conducted.

Study population
A total of 2131 individuals were surveyed in the Gannan Medical University Cohort from 2022 to 2023.After excluding individuals under 35 years old and those with incomplete data, the study included 2030 people aged 35 and above in Ganzhou, consisting of 637 males (31.38%) and 1,393 females (68.62%).The majority of participants were in the 50-64 age range, accounting for 1254 individuals (61.77%).Most had an educational level of junior high school or lower (70.74%).A vast majority were married, totaling 1,911 individuals (94.14%).The predominant occupation category was labeled as "other, " including 867 participants (42.71%) (Fig. 1, Table 1).

Distribution of different obesity types
Central obesity was the predominant form of obesity among the participants, accounting for 28.87% (n = 586) of the population.The results of the χ 2 test showed that sex, age, occupation, and educational level had a statistically significant impact on the distribution of obesity types (P < 0.05).However, marital status, smoking habits, and alcohol consumption did not significantly affect the distribution of obesity types (P > 0.05) (Table 2).

Prevalence of dyslipidemia and univariate analysis of each variable and dyslipidemia
In this study, 798 patients were diagnosed with dyslipidemia, resulting in a prevalence rate of 39.31%.After standardizing for the seventh census, the prevalence rate was adjusted to 36.51%.Statistical analysis using the χ 2 test revealed significant differences in dyslipidemia prevalence based on sex, age, family history of dyslipidemia, history of diabetes, smoking status, and type of obesity (P < 0.05), whereas marital status, educational level, occupation, alcohol consumption, and physical activity did not show any statistically significant differences (P > 0.05).Among the different obesity types, the highest prevalence of dyslipidemia was observed in individuals with compound obesity (58.79%).(Table 3).

Logistic regression analysis of the risk of dyslipidemia in various obesity types
Univariate logistic regression analysis indicated that both central obesity and compound obesity were associated with an increased risk of dyslipidemia (central obesity: OR = 2.10, 95% CI 1.72-2.57;compound obesity: OR = 3.04, 95% CI 2.18-4.23).However, no significant relationship was observed between general obesity and dyslipidemia.After adjusting for confounding factors like adjusted for Sex, Age, Marital status, Education, Occupation, Family history, Diabetes history, Smoking status, Drinking status, Physical activity, logistic regression analysis still demonstrated that central obesity and compound obesity remained risk factors for dyslipidemia Vol:.( 1234567890

Relationship between BMI and WC and risk of dyslipidemia by sex
Relationship between BMI and risk of dyslipidemia by sex Logistic regression analysis indicated a positive association between increased BMI and a higher risk of dyslipidemia in both men (OR = 1.287, 95% CI 1.210-1.368)and women (OR = 1.102, 95% CI 1.062-1.145).Furthermore, age and lifestyle were identified as additional factors influencing this relationship, and after adjusting for confounding factors, a restricted cubic spline model was employed to investigate the dose-response correlation between BMI and dyslipidemia.The results indicated a predominantly linear connection between BMI and the likelihood of dyslipidemia in both male and female subjects, with a statistically significant nonlinear trend (P < 0.05).When BMI was 24.43 kg/m 2 (OR = 1.30, 95% CI 1.01-1.68) in men and 23.66 kg/m 2 (OR = 1.01, 95% CI 1.01-1.02) in women, there was a significant increase in the prevalence of dyslipidemia.Moreover, this risk was found to be higher in men than in women.(Fig. 2, Table 5).

Relationship between WC and risk of dyslipidemia by sex
Logistic regression analysis revealed a significant association between WC and risk of dyslipidemia in both men (OR = 1.087, 95% CI 1.065-1.110)and women (OR = 1.039, 95% CI 1.024-1.054).Moreover, this relationship was found to be influenced by factors such as age and lifestyle.Following adjustments for confounding factors, a restricted cubic spline model was employed to examine the dose-response association between WC and dyslipidemia.The findings indicated a predominantly linear correlation between waist circumference and the likelihood of dyslipidemia in both male and female participants, with a significant nonlinear test result (P < 0.05).www.nature.com/scientificreports/When the WC was 88.77 cm in men (OR = 1.30, 95% CI 1.01-1.69)and 83.47 cm in women (OR = 1.02, 95% CI 1.01-1.02),there was a significant increase in the prevalence of dyslipidemia.Moreover, there was no significant difference in risk between men and women (Fig. 3, Table 6).

Discussion
This cross-sectional study utilized BMI and WC as assessment criteria to categorize individuals aged 35 and older in Ganzhou into several body figure categories and explored the association between different types of obesity and the likelihood of developing dyslipidemia.Furthermore, a restricted cubic spline model was constructed to examine the dose-response correlation between BMI, WC, and the risk of dyslipidemia across various sexes.The findings revealed that central obesity (OR = 1.99, 95% CI 1.62-2.44)and compound obesity (OR = 2.84, 95% CI 2.02-3.97)were significant risk factors for dyslipidemia among residents in Ganzhou.Moreover, the restricted cubic spline model indicated that when BMI approached the overweight threshold and WC fell within the early stages of central obesity, there was a significant increase in the risk of dyslipidemia.The findings revealed that the prevalence of dyslipidemia in Ganzhou was 39.31%, with an age-standardized prevalence of 36.51%.This prevalence was higher than the national average of 33.8% recorded from 2014 to 2019 10 and the 33.8% observed among adults in Shaanxi Province in 2018 20 .However, it was lower than the 42.05% prevalence found among adult residents in Inner Mongolia 21 .Additionally, the prevalence rate among Tajik adults in Xinjiang was 37.0% 22 and among adult residents in Qingdao in 2020 it was 40.53% 23 .The economic development level of Ganzhou City may intertwine with its unique Hakka culture, reflected in distinct cultural customs, eating habits, and lifestyles compared to other regions in the country.The prevalence of dyslipidemia in Ganzhou, possibly linked to the consumption of bacon and high-salt diets, highlights a significant health concern.To address this issue, the local government could consider implementing the 'three reductions and three healthy conditions' policies advocated by the National Health Commission, tailoring interventions to align with local Hakka culture and dietary practices to promote a healthier lifestyle for residents.Among residents aged over 35 years in Ganzhou, the prevalence rates of dyslipidemia were 41.67%, 49.66%, and 58.79% for general, central, and compound obesity, respectively.Notably, the prevalence of dyslipidemia among individuals with compound obesity was 58.79%.in Yangzhong City found that compound obesity posed the highest risk of dyslipidemia among residents aged 40-69.Similarly, a study of college students in Wuhu 26 revealed that both compound and central obesity increased the risk of cardiovascular disease and metabolic abnormalities in adolescents, with compound obesity showing the highest risk.The aggregation of risk factors in individuals with compound obesity may contribute to this heightened risk 27 .Moreover, the interaction between general and central obesity could exacerbate the impact of compound obesity on dyslipidemia.Therefore, it is important to consider both BMI and WC during disease screening and prevention.Notably, this study did not find a significant association between general obesity and the risk of dyslipidemia, which could be attributed to the limited number of individuals with general obesity in the study population.This study revealed that 37.59% of the residents in Ganzhou experienced various obesity issues, with a predominant occurrence of excessive WC.The findings from the restricted cubic spline model indicated a significant increase in the prevalence of dyslipidemia among individuals with pre-central obesity.Notably, there was no apparent disparity in dyslipidemia risk between sexes.Research has highlighted that individuals with central obesity tend to produce high levels of free fatty acids due to visceral fat accumulation, thereby promoting TG synthesis 28,29 .Additionally, visceral fat accumulation can alter lipase activity, enhance cholesterol synthesis, and contribute to the development of dyslipidemia.Moreover, the risk of dyslipidemia was notably elevated in men with a BMI of 24.43 kg/m 2 and in women with a BMI of 23.66 kg/m 2 compared to those with a normal BMI, with men exhibiting a higher risk than women.Numerous studies have demonstrated that being overweight or obese significantly increases the risk of dyslipidemia 30,31 .The observed sex differences could be attributed to the effect of estrogen on fat metabolism regulation and its role in inhibiting waist fat accumulation, potentially aiding women in reducing the risk of dyslipidemia associated with BMI 32,33 .The association between female BMI, WC, and the risk of dyslipidemia has been found to vary with age, and previous research has indicated that the impact of female BMI on TG and HDL-C levels changes as individuals grow older.Conversely, the relationship between male BMI, TG, and HDL-C levels remained consistent across different age groups, which aligns with the findings of this study 34 .Additionally, the link between male BMI, WC, and dyslipidemia risk is influenced by smoking habits, as tobacco smoke contains harmful substances such as nicotine, which can elevate free fatty acids in the bloodstream.This, in turn, promotes the synthesis of TG in the liver while reducing the activity of lipoprotein lipase, ultimately leading to higher TG levels and decreased HDL-C production 35 .www.nature.com/scientificreports/Multivariable regression analysis revealed no significant association between overall adiposity and dyslipidemia, contradicting the findings from the restricted cubic spline analysis.This study posits that the lower prevalence of general obesity among residents in southern Jiangxi may explain the lack of correlation between general obesity and dyslipidemia, suggesting that the type of obesity is influenced by both BMI and WC.The restricted cubic spline analysis specifically examines the relationship between BMI and dyslipidemia, revealing a non-linear association.Additionally, the logistic regression results for individual BMI and WC variables were in agreement with the findings from the restricted cubic spline analysis.
This study had several limitations.First, as a cross-sectional study, it could not establish a causal relationship between obesity type and dyslipidemia.Second, despite considering confounding variables such as general information, behavioral habits, and disease history, other influencing factors might still be unaccounted for.Thirdly, the sample size of individuals with general obesity in the study population is small, which might explain the lack of a significant association with dyslipidemia.However, this study addresses this limitation by utilizing a restricted cubic spline model to explore the dose-response relationship between BMI, WC, and the risk of dyslipidemia.This approach compensates for potential information loss due to the artificial classification of BMI and WC levels.Additionally, logistic regression was employed to adjust outcome-related parameters, enhancing the accuracy and objectivity of the final results.These findings provide valuable insights for the residents of southern Jiangxi seeking effective ways to prevent and manage dyslipidemia.
The prevalence of dyslipidemia and obesity among adult residents aged over 35 years in Ganzhou is a significant concern.Moreover, individuals with obesity and dyslipidemia face higher health risks.It is crucial to emphasize the importance of monitoring BMI and WC during disease screening and prevention.
years or older; (2) absence of mental illness and ability to cooperate with the investigation; and (3) signed informed consent.The exclusion criteria were: (1) secondary obesity; (2) severe heart, brain, kidney, or other fundamental diseases; (3) pregnancy; (4) incomplete physical examination indicators; and (5) incomplete biochemical indicators.This study adhered to the principles of the Declaration of Helsinki and was approved by the Medical Ethics Committee of Gannan Medical University (Ethics Committee Number: No.2019129).All participants volunteered for the study and provided written informed consent.

Figure 1 .
Figure 1.Flowchart of participants who were included in the study.

Figure 2 .
Figure 2. Dose-effect relationship between BMI and risk of dyslipidemia in different sex.

Figure 3 .
Figure 3. Dose-effect relationship between WC and risk of dyslipidemia in different sex.

Table 1 .
Basic information of the survey population.

Table 2 .
General situation and distribution of different obesity types.This study suggests that individuals with compound obesity have a higher risk of dyslipidemia than nonobese individuals.Research by Huang et al.

Table 3 .
Univariate analysis of variables and dyslipidaemia.

Table 4 .
Logistic regression analysis of various obesity types and dyslipidemia.a: unadjusted; b: adjusted for Sex, Age, Marital statu, Education, Occupation, Family history, Diabetes history, Smoking status, Drinking status, Physical activity.

Table 5 .
Relationship between BMI and risk of dyslipidemia by sex.Adjusted for Age, Marital status, Education, Occupation, Family history, Diabetes history, Smoking status, Drinking status, Physical activity.

Table 6 .
Relationship between WC and risk of dyslipidemia by sex.Adjusted for Age, Marital status, Education, Occupation, Family history, Diabetes history, Smoking status, Drinking status, Physical activity.